Biometric Recognitionin Surveillance Environments

Project Description

The acronym of this project comes from the Latin and summarizes its goals: "Quis" stands for "Who is" and "Campus" refers to a delimited space. Hence, in this project we aim at research and development of a biometric recognition system able to work in completely covert conditions. The main idea is that whenever a subject enters a QUIS-CAMPUS, it is automatically recognized, using multiple biometric sources and without requiring any active participation from the subject side.

Given the efforts being held into extending robust biometric recognition techniques to in the wild scenarios, we propose a fully automated surveillance system for human recognition purposes, attained by combining human detection and tracking, further enhanced by a PTZ camera that delivers data with enough quality to perform biometric recognition. We understand that despite being a very attractive goal, human identification in the surveillance context had remained an open problem so far. Nonetheless, we strive to make Quis-Campi a reference in this field.

Project Demonstration

A demonstration video of our automated surveillance system.

Report on the current status of QUIS-CAMPI prototype. Working in an outdoor visual surveillance scenario, with multiple subjects in the scene. The system detects and tracks the human body silhouettes, predicting their positions and pointing a PTZ camera that collects data of subjects faces up to 50 meters.

The Quis-Campi Dataset

The first biometric dataset automatically acquired in a real-surveillance scenario!

Gallery Data

The Quis-Campi dataset gathers biometric data acquired from over 320 participants, including high-quality registration images (a) along with 6 videos of the gait sequence filmed under different angles (b), as well as a 3D model of the face (c).

Probe Data

A master-slave surveillance system has been used to automatically acquire face images (d) and videos (e) of the subjects roaming around on surveillance environments.

Evaluation Protocol

Verification (Model Selection)

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Verification (Performance Reporting)

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Split 10

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Download QUIS-CAMPI

The QUIS-CAMPI dataset is publicly available for the research community.
In order to request one copy of the database, send an email to
jcneves@penhas.di.ubi.pt with the following information: